Study information

# Data Analysis in Social Science 3

Module title Data Analysis in Social Science 3 SSI3003 2023/4 15 Dr Alexey Bessudnov (Convenor)
 Duration: Term Duration: Weeks 1 2 3 11
 Number students taking module (anticipated) 60

## Module description

Basic knowledge of statistics and data analysis is often not enough for dealing with more complicated problems in the social sciences, as well as in market research, applied policy analysis, and data-driven journalism. This module introduces you to more advanced techniques for social data analysis using the statistical programming language R and the tidyverse framework (or alternatively Python and pandas). These techniques are especially useful while working with large and “messy” data sets. While some statistical theory is covered in this module, the discussion of statistical concepts is generally non-mathematical and intuitive and is based on numerous examples from social sciences. The module assumes familiarity with basic descriptive statistics and linear regression analysis.

## Module aims - intentions of the module

The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data. More
specifically, you will learn how to clean, transform, reshape and visualise data in R, a statistical programming language, and
tidyverse, a collection of tidyverse packages. You will also learn the fundamentals of programming in R, such as conditional
statements, loops and functions. After completing this module, you will be able to independently conduct data analysis in R.
Employers in many industries value this skill.

## Intended Learning Outcomes (ILOs)

### ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. clean and prepare your data for statistical analysis in R or Python;
• 2. conduct statistical analysis using selected methods at the advanced level in R or Python;

### ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. apply statistical data analysis techniques to social science problems;
• 4. clearly explain the results of statistical analysis in substantive terms and relate them to substantive social science problems;

### ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. report the results of statistical analysis in writing in a way that would be understood by non-specialists; and
• 6. use general-purpose statistical software for the analysis of social data at the advanced level

## Syllabus plan

Whilst the module’s precise content will vary from year to year, it is envisaged that the syllabus will cover some of the following themes:

• Data types and structures in R or Python
• Data import with readr and data.table / pandas
• Data manipulation with dplyr / pandas
• Data visualisation with ggplot2 / matplotlib
• Iteration
• Functions
• Reproducible research and effective presentation of statistical results

## Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
221280

## Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity2211 x 2 hour lectures / computer lab sessions
Guided independent study78Reading and preparation for lectures and lab sessions
Guided independent study50Data analysis and writing of the data report

## Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Github assignments2 Github assignments (5 problems each)1-6Written feedback via Github
Mock ELE test5 questions on ELE (about 30 minutes)1-6ELE feedback

## Summative assessment (% of credit)

CourseworkWritten examsPractical exams
10000

## Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
ELE test501 hour1-6ELE feedback
Data report501,500 words1-6Written feedback
0
0
0
0
0

## Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
ELE testELE test (1 hour)1-6August/September reassessment period
Data reportData report (1,500 words)1-6August/September reassessment period

## Indicative learning resources - Web based and electronic resources

### Key words search

Social science, quantitative, data analysis, statistics, R

Credit value 15 7.5 SSI1005 and SSI1006 SSI2005 if not taken before 6 No 30/03/2016 06/10/2022